3D Face Alignment

12 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

1adrianb/face-alignment ICCV 2017

To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets.

FacePoseNet: Making a Case for Landmark-Free Face Alignment

fengju514/Face-Pose-Net 24 Aug 2017

Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.

Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

choyingw/SynergyNet 19 Oct 2021

Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.

Learning an Animatable Detailed 3D Face Model from In-The-Wild Images

YadiraF/DECA 7 Dec 2020

Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.

Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge

1adrianb/face-alignment 29 Sep 2016

This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge.


rlit/LatentRANSAC CVPR 2018

We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.

Hierarchical binary CNNs for landmark localization with limited resources

1adrianb/binary-networks-pytorch 14 Aug 2018

To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment.

Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild

HongwenZhang/JVCR-3Dlandmark IEEE Transactions on Image Processing 2019

Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector.

Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment

LeiJiangJNU/DAMDNet 30 Aug 2019

3D face alignment of monocular images is a crucial process in the recognition of faces with disguise. 3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference. This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework. We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism.

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

vitoralbiero/img2pose CVPR 2021

Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators.